It's a nice article that basically (rage)baits the readers before they realize the author actually disagrees with the premise of coding being solved. So here goes, my higher-level rant on "solving" something with AI.

I strongly dislike all the "X is solved" narratives. What does it mean for something to be solved? A math problem (or any kind of problem), a riddle, a mystery, a dispute. Those are all instances of a particular situation that requires a "solution", but new situations will always come up. I understand that by "cancer/coding/X is solved" in this new age of (gen)AI that means the ability to streamline or speed up the "solution finding" procedure, but even that presupposes a fixed, static, fully deterministic space of the things we are trying to "solve".

Even cancer cannot be fully solved - Demis Hassabis slowly started drifting away from using the word "diseases can be solved" because they can't be eliminated - we can only speed up the process for finding a cure for any particular disease, be it existing or new and/or evolving.

Is bridge-building solved? Architecture? Why are architects still employed? Is solving "civil engineering" or designing an optimally running machine a thing to be solved? What are we trying to do when we talk about "progress with AI"? Even when the "recursively-self-improving-AIs" and "perfect" robots do arrive, we're still bound to work with them, and they'll have to evolve to find new solutions to new problems.

To be clear, I work with and rely on LLMs every day, from coding custom RAG architectures with CC and Pi to research and agentic data science. These bombastic conversations, however need to quiet down a bit so we can get back to work :)